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Characterizing sampling and quality screening biases in infrared and microwave limb sounding

机译:在红外线和微波肢体探测中的采样和质量筛选偏差

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摘要

This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected.
机译:本研究研究了轨道采样偏差,并评估了用于被动大气探测器(MIPAS)和Aura微波Lemb发声器(MLS)的迈克森干涉仪对Michelson干涉仪引起的额外影响。 MIPAS充当典型的红外肢体发射探测器的代理,而MLS则作为微波肢体发声器的代理。通过将模型字段内插到真实的采样模式,并另外,根据其相应的质量标准的指示筛选这些偏差来计算温度和几个痕量气体。这两种仪器都有密集的均匀采样模式,典型的肢体发射探针,产生几乎相同的采样偏差。然而,在丢弃的位置数量之间存在显着差异。 MIPAS作为中红外仪器,对云非常敏感,因此受到它们影响的测量从分析中拒绝。例如,在热带地带中,MIPAS产量受到云的强烈影响,而MLS大多不受影响。

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